Spiking graph neural networks (SGNNs) have attracted considerable attention due to their high efficiency and low energy consumption in processing graph-structured data. In such networks, the firing threshold of neurons serves as a critical gating mechanism that governs spike sparsity, information flow, and energy consumption. However, existing threshold mechanisms are either fixed global constants or rely solely on coarse-grained statistics such as node degree. These approaches fundamentally fail to capture the rich local structural heterogeneity inherent in graph-structured data. In such data, nodes naturally assume distinct structural roles. Consequently, the resulting spike trains retain insufficient discriminative structural information. This deficiency compromises model capability and robustness against local structural distribution shifts. To address this issue, we propose a structure-adaptive threshold learning framework based on sparse representation. The framework learns a dictionary in which each atom captures a typical local subgraph pattern and is associated with a learnable threshold. Soft weights derived from sparse coding are leveraged to aggregate the atomic thresholds via weighted averaging, yielding a node-specific firing threshold. Furthermore, we design an alternating soft-fusion-hard-grouping training strategy that decouples structure-aware threshold generation from pattern-specific threshold optimization. Experimental results on multiple benchmark semi-supervised node classification tasks demonstrate that the proposed method significantly improves classification accuracy while preserving high spike sparsity. These results validate the effectiveness of the structure-adaptive threshold mechanism for low-power spiking graph learning.
Zehan Li, Yingyi Li, Juntao Zhang et al.· International Journal of Inf...· 0 citations
: Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks (GNNs) at prohibitive costs. Crucially, neither method treats structural information as learnable knowledge that can be automatically acquired from data and explicitly represented. To bridge this gap, we propose SparseGNN, a structural-knowledge-enhanced framework for anomalous user detection. It regards atomic subgraph patterns as fundamental, learnable units of structural knowledge. This framework is concatenated with original node features and fed into any standard GNN, without modifying the backbone architecture. Experiments on real-world datasets demonstrate that SparseGNN improves the accuracy and F1-score of standard GNNs for anomalous users detection without requiring predefined patterns, while maintaining linear complexity. Because the learned atomic patterns capture global high-order topology, the resulting structural knowledge representation is inherently less sensitive to localized edge perturbations, incidentally conferring improved stability under adversarial structural attacks.
Zehan Li, Yingyi Li, Zhiwei Tang et al.· Computers, Materials & C...· 0 citations